Data Management Lessons from the RAMP Sensor Network скачать в хорошем качестве

Data Management Lessons from the RAMP Sensor Network

Presented By: Carl Malings, NASA Description: The Real-time Affordable Multi-Pollutant (RAMP) sensor package is a low-cost air quality monitor combining five internal gas sensors, external or built-in optical particle mass sensors, battery power, and cellular data communication. More than 60 RAMPs have been deployed across four continents since 2017, generating more than 50 gigabytes of raw data. Alongside the sensors themselves, a system has been developed for storing, cleaning, and processing their data. Data analytics include the application of traditional regression methods as well as more complicated machine learning schemes to calibrate low-cost sensor data to regulatory-grade instruments. Analyzing data and drawing conclusions presents additional challenges. This presentation will outline our system, as well as the scientific and practical lessons learned during its development. Deployments of the RAMPs have varied from dense networks in traditionally monitored urban areas (Pittsburgh, PA) to individual sensors deployed in previously unmonitored regions (e.g. Niger, Rwanda, Ghana). For example, in Kigali, analysis of daily and weekly variability during the dry and wet seasons allowed a tentative attribution of pollution sources for fine particulate matter. The capabilities of electrochemical gas sensors to respond quickly to concentration changes, e.g. to chart vertical profiles using a balloon-mounted RAMP, has also been assessed. Efforts to integrate RAMP network data with other data sources, including existing regulatory monitoring networks (to track sensor performance over time and automatically correct for drift) and remotely sensed satellite aerosol data will also be summarized. Open areas of system development and perceived future data needs will also be discussed, including the need to better integrate stationary and mobile data. Overall, lessons learned in the deployment and management of the RAMP sensor network provide useful insight to future air quality sensing efforts using low-cost sensors.